What Difference Does it Make? Implications of the Size of the Difference between the Means of Two Groups
Why this work is in the frame
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Bibliographic record
Abstract
This study employed a model with two normal distributions of scores to study group differences and similarities as a function of the distance between group means. The mean of one distribution was systematically moved further and further away from the mean of the other. A table was developed of associated measures of group overlap and separation, e.g., r, eta, percent correct classification, percentage of overlap, for several levels of d' (difference between means expressed in standard deviation units). The implications of various d' values for the selection of extreme groups, e.g., top 1% of scorers, were discussed. It was noted that even small differences between means could result in an extremely disproportionate inclusion of members from the two original groups in a high-scoring subgroup.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it